Magnetic Resonance (MR) images are acquired during medical examinations on Magnetic Resonance Imaging (MRI) apparatus and stored in digital form in image archives. The digital data of the MR image can be stored and transferred to other image display, analysis, and archiving systems by means of a computer network. The digital data is typically transmitted over a communication network using a DICOM Standard (Digital Imaging and Communications in Medicine (DICOM), Part 3: Information Object Definitions, PS 3.3-2001).
The acquired pixel values in an MR image do not relate directly to the brightness and contrast of display for diagnostic use of the image, and frequently no information is provided about ideal window width (contrast) and window level (brightness) that should be applied to the image for this purpose. If the window width and level are present, very often this information is not optimal for diagnostic use. Therefore radiologists or technicians usually need to manually adjust window width and window level of these MR images (often referenced as window-level (W-L)) before the diagnosis can be performed on a display workstation. The manual adjustment procedures require the user's time and energy, particularly if a large number of MR slices are acquired for the patient. Even a single study may include hundreds or thousands of individual images, grouped into several series for different spatial views and/or acquisition parameters. Since the characteristics of the images in a given series are similar, some medical personnel in order to be efficient usually adjust W-L not for each slice individually but only for the most clinically important one in each series (often this is a slice in the middle of the acquired volume, or close to it). However, when W-L values are set to the same value for all images in the same series, the results may not be optimal for all the images and may require additional adjustment for diagnostic use.
Another consideration is that the range of gray scale used in conventional workstation monitors (typically, 8 bits) is oriented to the sensitivity of the human eye to small differences in light levels. However, the original image often has 1024 to 4096 or more gray levels (i.e., 10–12 or more bits). Therefore, a manual adjustment must be made without loosing diagnostically valuable features of the MR image. This is typically not a trivial task, particularly if there is more than one area of diagnostic interest within the image, and also when those areas significantly differ in intensity dynamic range. For these reasons, radiologists and MR technicians often differ in their subjective opinions about the W-L setting for the presentation of an image with best image quality. For MRI, there is no unanimous agreement on the best presentation of this data, so a universal solution of the problem of picking W-L values does not exist. This is often referred to as the “Window-Level problem” or “W-L problem”.
Another consideration of the W-L problem are the characteristics of MR images. For example: (1) MR images are measurements of very low energy quantum processes. As such, the images are often noisy, with maximal and minimal pixel intensity values that vary from image to image, and also within a particular image, e.g. spikes and background noise. (2) There are different types of possible MRI acquisitions, for example, T1 or T2 weighted series. Each type of acquisition may require different parameters for optimal image display. (3) Image acquisition may be done with either (i) transmitting/receiving coils that surround the part of the patient being imaged (for example, head, extremity, or whole body) or (ii) surface coils for structures such as the neck and spine. (4) The size and mass distribution of individual patients can affect the values obtained from an MRI acquisition in complex ways. (5) Different users may prefer different “looks” of images with regard to image brightness and contrast. (6) The display monitor type (e.g., regular, flat panel), age (e.g., old, new), settings (e.g., gamma curve, etc.) have an effect on how an image area is displayed on a monitor.
One method to automatically set W-L values is to determine the maximal and the minimal intensity values of each MR image, and then to map the values to an output range [0, 255] in a linear fashion. This method, which is readily calculatable, does not work well for many MR images, in particular, for those acquired with surface coils.
There have been prior art efforts to automate the manual adjustment procedures to address the Window-Level problem for different medical modalities, (e.g. X-ray, CT, MRI). Those efforts may be classified into three groups. The first group comprises methods wherein the operator participates in image quality evaluation to find the W-L values. The second group comprises semi-automatic methods wherein the operator provides some input or are based on values that have been determined empirically from operator experience. The third group includes adaptive and automatic methods.
With regard to the first group, one known approach is sometimes referred to as “static contrast windowing”. This approach places the midpoint of the 8-bit dynamic range at the average intensity value of the region of interest (ROI) in the image, which is usually defined with operator assistance. The intensity values that are below the contrast window are presented as black on the display and values above the contrast window are displayed as white. Intensity values within the contrast window are mapped to corresponding display brightness values using a straight-line transfer curve. Contrast windowing is “static” when the window and the transfer curve are fixed for the conversion of the entire image data array. Details within the intensity range of the contrast window are shown on the display using the 8 bits of dynamic range. The remaining portion of the image, however, are displayed as either “too white” or “too black”, with no details perceivable in the presentation. When other areas of interest are not represented properly at the selected W-L setting, more than one presentation of the image may be needed, each with a different contrast window to properly represent important anatomical details at all brightness levels.
Another approach sometimes used in X-ray CT practice employs a transfer curve having two contrast windows applied to a single image. This dual window approach uses two grayscale ramps or “windows” in a single display, where one window is set to encompass the brightness levels around bone and the other window is set to cover the brightness levels around soft tissue. With this approach, the anatomy shown in CT images is generally known to the user. As such, a trained user knows a priori what region of an image is soft tissue and what is bone, so that the simultaneous display of two gray ramps in the same image is not confusing to the user. However, in any anatomic regions which are intermediate in brightness between the windowed soft tissue and bone, the X-ray CT image values may be displayed using parts of both grayscale ramps, which can be very confusing. This limits the applicability of dual ramp presentation of CT. A common method of displaying such images uses separate presentations of the same image using the full bone range applied to the image in one display area, and the soft tissue range is applied to another image in a separate display area. However, neither a dual-ramp presentation or multiple presentations with fixed W-L settings may be acceptable for MRI, since the digital values of images are not calibrated to be independent of the individual examination as they are in CT. As discussed above, the proper Window-Level for any particular image depends on a variety of factors that vary from image to image, from series to series, and from patient to patient.
One technique intended to enable the operator to select a static window is described in U.S. Pat. No. 5,042,077 (Burke). The medical imaging equipment produces an image of the subject under study and presents a graph of the image's histogram, which indicates the distribution of brightness levels of the image pixels. Using a trackball, the operator manipulates a contrast window which is displayed on the histogram and which enables the operator to select brightness ranges in the image for contrast enhancement.
With regard to the second group (i.e., the semi-automatic algorithms), U.S. Pat. No. 5,900,732 (Felmlee) relates to an automatic windowing method for MR images. The method includes producing a histogram of the reconstructed image; removing histogram bins with less than a threshold (T) number of pixels; calculating a window level by determining the mean intensity of the remaining histogram bins; and calculating the window width WW according to a fixed relationship. While this method may have achieved a certain degree of success in its particular application, the method requires a manual step of setting the threshold T, and therefore, requires the involvement of an MRI technician or radiologist in the task of setting the W-L.
U.S. Pat. No. 5,835,618 (Fang) relates to a method for dynamic range re-mapping for optimum image display. This method employs a user-defined parameter for both additive and multiplicative algorithms, as well as a “mixing” algorithm for the dynamic range re-mapping, thus requiring an involvement of medical personnel into the technical details of adjusting W-L.
U.S. Pat. No. 5,357,549 (Maack) relates to a method of dynamic range compression of an x-ray image wherein a non-linear mapping function is employed to determine the equalization value for each pixel in the image. This method's final output is dependent on a lowpass filtered signal from the local neighborhood around each pixel. A disadvantage of this method is the limitation of being able to display only one range of image data that is of primary interest with the best gray scale presentation; a disadvantage particularly if more than one range needs to be displayed simultaneously.
One non-linear method for image intensity correction in MRI is described in Handbook of Medical Imaging, 2000. This method is based on intensity non-uniformity (INU) field estimation using the log-transformed image histogram as a probability density function (PDF) approximation. This field is iteratively smoothed with a B-spline function and is used for intensity correction by means of mapping the original histogram to the one sharpened by deconvolving a Gaussian blurring kernel.
With regard to the third group (i.e., adaptive or automatic algorithms), one known method for mapping data brightness levels to a display having a limited dynamic range is known in the art as adaptive contrast enhancement. One variant is “adaptive histogram equalization” (AHE). Unlike methods which are “static”, the AHE method does not employ a fixed contrast window for the entire image. Instead, the AHE method looks at each datum intensity value in the acquired data array one at a time and compares it with the values in a local surrounding spatial area, or “context region”. The length and width of the context region may, for example, range from one sixth to one sixtieth of the length and width of the entire image data array. While there are many variations on the calculations employed with this method, generally the centered datum value is mapped to a display brightness, which provides contrast with respect to the other data values that are generally within the same context region. The calculations are performed, in principle, at each pixel location in the image data array with respect to its surrounding context region and the method is, therefore, computationally intensive. The AHE method and some variations are described in “Algorithms For Adaptive Histogram Equalization”, Pizer, S. M., Austin, J. D. et al., SPIE Vol. 671, Physics and Engineering of Computer Multidimensional Imaging and Processing, 1986.
U.S. Pat. No. 5,305,204 (Ohhashi) relates to a digital image display apparatus with automatic window level and window width adjustment. With this apparatus, a pixel value of a digital image data is converted into brightness by using a suitable conversion function, and displaying an image within an optimum window. This pure neural network based method may not be reliable or accurate when encountering a new kind of input image which has not been seen in the training set of the neural network.
Another method which employs a neural networks for automatic window-level adjustment in MRI is disclosed in U.S. Pat. No. 5,995,644 (Lai). A further method which employs a neural networks is disclosed in U.S. Pat. No. 6,175,643 (Lai).
U.S. Pat. No. 5,268,967 (Jang) relates to a method for automatic foreground and background detection in digital radiographic images. The method includes the step of detecting edges in the image signal based on a morphological edge detector.
Accordingly, while the apparatus and methods described above may have achieved certain degrees of success in their particular applications, the manual methods in W-L adjustment can lead to variations in image quality because individual operators have different opinions regarding a preferred presentation of specific MRI images data. In addition, the manual adjustment requires time, therefore reducing throughput of the medical imaging system for diagnosis. The semi-automated methods employ manual selection of parameters or require the operator to evaluate one or more criteria of an image or study. Prior art adaptive methods may also need some operator participation or at least some prior information to train the system, which produces final W-L parameters for display.
As such, there exists a need for an apparatus and method which overcomes the problems described above. The present invention is directed to overcoming the problems described above. In particular, the present invention is directed to a fully automated method. The method is based on modeling radiologist's criteria for the appearance of MR images and comprises of a sequence of steps in W-L adjustment. This method performs both spatial image and histogram analysis and processing, and can include the steps of obtaining of the body part or coil type information, extraction of the region of interest (ROI) within the original digital image, a W-L definition based on pixel value distribution analysis within extracted ROI, and linear or non-linear re-mapping of the original image range of intensities to the gray scale range of the display.